Classification and Regression Tree (CART) analysis to predict influenza in primary care patients. Issue 1 (December 2016) - Zimmerman, Richard; Balasubramani, G.; Nowalk, Mary; Eng, Heather; Urbanski, Leonard; Jackson, Michael; Jackson, Lisa; McLean, Huong; Belongia, Edward; Monto, Arnold; Malosh, Ryan; Gaglani, Manjusha; et al.
Abstract Background The use of neuraminidase-inhibiting anti-viral medication to treat influenza is relatively infrequent. Rapid, cost-effective methods for diagnosing influenza are needed to enable appropriate prescribing. Multi-viral respiratory panels using reverse transcription polymerase chain reaction (PCR) assays to diagnose influenza are accurate but expensive and more time-consuming than low sensitivity rapid influenza tests. Influenza clinical decision algorithms are both rapid and inexpensive, but most are based on regression analyses that do not account for higher order interactions. This study used classification and regression trees (CART) modeling to estimate probabilities of influenza. Methods Eligible enrollees ≥ 5 years old (n = 4, 173) who presented at ambulatory centers for treatment of acute respiratory illness (≤7 days) with cough or fever in 2011–2012, provided nasal and pharyngeal swabs for PCR testing for influenza, information on demographics, symptoms, personal characteristics and self-reported influenza vaccination status. Results Antiviral medication was prescribed for just 15 % of those with PCR-confirmed influenza. An algorithm that included fever, cough, and fatigue had sensitivity of 84 %, specificity of 48 %, positive predictive value (PPV) of 23 % and negative predictive value (NPV) of 94 % for the development sample. Conclusions The CART algorithm has good sensitivity and high NPV, but low PPV for identifying influenza among outpatients ≥5 years. Thus, it is good at identifying a group who do not need testing or antivirals and had fair to good predictive performance for influenza. Further testing of the algorithm in other influenza seasons would help to optimize decisions for lab testing or treatment.